In today's rapidly growing technological workspace, businesses have more data than ever before.
Having mass amounts of data means nothing; what you do with that data is what matters. That's where data mining comes in. It makes sense of data as businesses work to implement various goals and potential improvement strategies through the process of turning raw data into actionable insights. There are many ways to go about this, and it all comes down to the data mining techniques your business chooses to use.
Data mining is the process of finding and detecting patterns in data for relevant insights; the various techniques are how you go about turning raw data into accurate observations.
Common Data Mining Techniques:
A variety of data mining techniques are often required to uncover insights that lie within big datasets, so it would make sense to choose more than one. While data mining can segment customers, it can also help determine customer loyalty, identify risks, build predictive models, and much more.
Most, but not all, data mining techniques either fall under the statistical analysis or machine learning category, depending on how they are used. Below, we dive more into each technique.
A necessary technique when it comes to data mining is data cleaning. Raw data must be cleaned, formatted, and analyzed for it to be useful and applied to different types of analytical methods. This technique is part of different elements of data modeling, transformation, aggregation, and migration.
How is data cleaning used?
Businesses use data cleaning as a first step in the data mining process because otherwise, the data found is useless and unreliable. There needs to trust in the data and the results that come from data analytics, for there to be a worthwhile and actionable next step. Data cleaning is often the first step that is conducted in the data mining process.
One data mining technique is called clustering analysis, otherwise referred to as numerical taxonomy. This technique essentially groups large quantities of data together based on their similarities. This mockup shows what a clustering analysis may look like.
Data that is sporadically laid out on a chart can be grouped in strategic ways through clustering analysis. This analysis can also act as a preprocessing step, which means data is formatted in a way so other techniques can be easily applied.
When it comes to clustering approaches, there are five major methods used by data scientists:
- Partitioning algorithms: creating various partitions and then evaluating them based on specific criteria
- Hierarchy algorithms: creating a hierarchical disposition of the data set using specific criteria
- Density-based: based on connectivity and density functions
- Grid-based: based on multiple-level granularity structures
- Model-based: a model is first hypothesized for each of the clusters, then the best fit of the model is found
Going hand-in-hand with these clustering approaches are five clustering algorithms used to classify each data point into a specific group. Data points within the same group have similar properties or features.
These algorithms are:
K-Means Clustering: groups observations into clusters where each data point is part of the cluster with the nearest mean
Mean-Shift Clustering: assigns the data points to the clusters iteratively by shifting points towards the mode. Most commonly used in image processing and computer vision.
Density-Based Spatial Clustering of Applications with Noise (DBSCAN): groups together data points in a specific space that is close together while marking specific outlier points in low-density regions within the cluster. Frequently cited in the scientific literature.
Expectation-Maximization (EM) Clustering with Gaussian Mixture Models (GMM): used to cluster unlabeled data as it accounts for variance (width of a bell curve) to determine the shape of the distribution or cluster
Agglomerative Hierarchical Clustering: works to build a hierarchical analysis of the clusters with a bottom-up approach. Each observation will start in its own cluster, and pairs of clusters are merged as one moves up the hierarchy
What is clustering used for?
There are a few ways to draw knowledge out of clustering analysis. Insurance companies can identify groups of policyholders with high average claims. Clustering can be used in marketing to segment customers based on the benefits they'll experience when purchasing a specific product. Another example of clustering is how seismologists can see the origin of earthquake activity and the strength of each earthquake, then apply that insight for designing evacuation routes.
Classification is often referred to as a subset of clustering. The classification consists of analyzing various attributes that are associated with varying types of data. When a business can identify the main characteristics of these data types, they can better organize and classify all data that is related.
This is a vital part of identifying specific types of data, like if a business wants to further protect documents with sensitive information, like social security or credit card numbers.